Optimal allocation and Contingency Analysis of Embedded Generation Deployment in Distribution...

2012 International Conference on Computing, Electronics and Electrical Technologies [ICCEET]

Optimal allocation and Contingency Analysis of

Embedded Generation Deployment in Distribution

Network using Genetic Algorithm

Venkataramana Veeramsetty PG Scholar

G.V.Naga Lakshmi Assistant Professor

A.Jayalaxmi Associate Professor

Dept. of Electrical Engineering Osmania University, Hyderabad

Andhra Pradesh, India e-mail:[email protected]

Dept. of Electrical Engineering Research Scholar in Osmania

University, Hyderabad, A.P.India e-mail: [email protected]

Dept. of Electrical & Electronics Engineering, JNTU, Hyderabad

Andhra Pradesh, India e-mail:[email protected]

Abstract-This paper presents a contingency analysis study of

Embedded Generation (EG) deployment in distribution network. It is expected of increasing amounts of EG that will be connected to power system distribution in the near future. Due to advances of technology and deregulation in the power market, EG is

expected to become more cost effective energy sources that open the opportunity of exploiting renewable energy sources. This paper is started by introducing a new method for determining the optimal location and size of EG that needed to be deployed in the

distribution network simultaneously using Genetic Algorithm (GA) technique with topology based distribution load flow solution. It follows by the discussion of evaluating the impact of the location and the size of EG to the system before and after

contingency is created in the system due to fault. The proposed allocation methods and contingency analysis are demonstrated using IEEE 69-bus radial distribution system.

Keywords-contingency analysis, distribution system,Eembedded Generation (EG), Genetic Aalgorithm (GA), topology based load flow solution.

I. INTRODUCTION

The advances in technology have created a new trend for the growth of Embedded Generation (EG). The generation facilities being huge plants (hundreds MW) were usually located at far off places, away from populated areas, but the advancement of EG technology in a more cost effective way has led to the generation in the vicinity of the area where power is consumed. Addition of EG, will create much impact on system losses [8]. In addition, the transformation of electric power market into deregulation in many countries to end the monopoly of the vertically integrated power utilities, the increasing reliability expectations and inability to add new transmission lines. These factors become a driving force for the growth of EG.

Embedded Generation (EG) also provides an opportunity to effectively exploit the renewable energy, which is produced from replenish able resources available and abundant in nature. To accommodate this new type of generation, the existing distribution network should be utilized and developed in an

978-1-4673-0210-4112/$31.00 ©2012 IEEE 86

optimal manner. Several approaches have been proposed in literature to determine the optimal location of EG in the distribution networks. To date, Artificial Intelligence (AI), evolutionary computation and optimization techniques are among the popular techniques that are normally used to solve these problems. The optimal allocation problem can be solved using Ant Colony Search Algorithm (ACSA). This algorithm is inspired from the natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. The property of ACSA or Ant Colony Optimization (ACO) is adapted to solve the optimal location and size of EG. However, the rates of ant colony regulating parameters need to be determined using experimental approach. The complete approach is proposed in [1 ].

The optimal allocation problem is also solved by using Evolutionary Programming (EP) method. In this, authors use the sensitivity indices as the tools to predict the placement of EG at a particular bus. Sensitive indices are developed from voltage stability index. Buses with highest sensitivity values are selected for the location of the embedded generators [2]. The incorporation of Particle Swarm Optimization (PSO) for distribution generation sizing and allocation is proposed by simultaneously taking losses as objective function along with load flow [3]. The related works on GA for optimal allocation of EG using ET AP software with MA TLAB for solving that problem has been proposed in [4]. However, the implementation of GA is not explained clearly. When Distributed Generation is combined with shunt capacitor placements computation time is more[5].Different evolutionary techniques for optimal operation of distributed generation in terms of cost of Active and Reactive powers are GA, differential evolution, ACO, PSO and tabu search. A comparison of these methods is proposed in [6].

This paper solves optimal allocation and contingency analysis problem by using simple load flow solution with continuous genetic algorithm with PQ model of embedded generation. When compared to other methods in this method convergence is guaranteed and computation time is less.


1.2 EG Model

Generally, EGs in distribution networks can be modeled as follows:

1. PV Model 2. PQ Model

Since distribution networks are unbalanced three phase systems, EGs can be controlled and operated in two forms:

1. Simultaneous three phase control 2. Independent three phase control or single phase control.

With respect to control methods and DG aspects, four models can be developed for simulation of Embedded generations:

P+jQ

Figure I. PQ model with simultaneous three-phase control

Pc�iQc

Figure 2. PQ model with independent three-phase control

Figure 3. PV model with simultaneous three-phase control

87

V Lck c

Figure 4. PV model with independent three -phase control

However, in this paper only PQ node model is considered.

II. LOAD FLOW SOLUTION OF RADIAL DISTRIBUTION

NETWORKS

Load flow analysis of distribution systems has not received much attention unlike load flow analysis of transmission systems. However, some work has been carried out on load flow analysis of a distribution network but the choice of a solution method for a practical system is often difficult. Generally distribution networks are radial and the RlX ratio is very high. Because of this, distribution networks are illconditioned and conventional methods like Newton-Raphson (NR) and Fast Decoupled Load Flow (FDLF) are inefficient at solving such networks.

In many developing Countries like India, all the 11 KV rural distribution feeders are radial and too long. The voltages at the far end are very low with very high regulation.

The proposed method involves only evaluation of a simple algebraic expression of voltage magnitude and no trigonometric terms. Topology based approach is used for evaluating equivalent load at every node. This eliminates the complex process of identifYing nodes connected beyond a particular node. The two developed matrices; 'bus injection to node power matrix [10)' and 'line loss to node power matrix [10)' are very easy to form. The features of this method are robustness and computer economy. Convergence is always guaranteed. The assumption is that shunt capacitance is negligible at the distribution voltage level.

The three-phase radial distribution networks are assumed to be balanced and can be represented by their equivalent single line diagrams. This assumption is quite valid for 11 kV rural


distribution feeders in India and elsewhere. The procedure how we solve the load flow problem is given below.

First read the system data by assuming V1= 1.0 pu. Line losses are assumed to be zero in the first iteration. Obtain effective values of active power and reactive power by using,

[N] = [BINP][S] Where, S is the column matrix which represents the

complex power of all loads. Receiving end voltages are calculated using simple formulae V2=...j (BUl-Au]) and power losses of all branches using SLOSS =PLOSS + JQLoss. Multiply the power loss column matrix SLOSS with LLNP matrix to get N' matrix. N' represents that part of line losses in the effective load at various nodes. Now Effective load at each node becomes N + N'. Same procedure is repeated until the difference between voltages of present and previous iteration is of low value. The formulas which are used for measuring voltages and losses are taken from [9].

Suppose EG with generating capacity Sg =P g +jQg is connected to Bus 3 where load is SL =PL +j QL Effective load on Bus 3 = (PL � PG) + j (QL � QG)

Bus no 3

Figure 5. Effective Load on Bus 3

III. GA FOR OPTIMAL ALLOCATION OF EG

In this section, continuous GA is proposed to find the optimal size and location of EG units in the distribution system to minimize the total loss in the system. By minimizing the loss, the voltage profile at each bus is expected to be improved. This method requires load flow to be run several times. After finding the best location and the size simultaneously for EG, the algorithm is completed. The objective function H is the minimization of total loss of the system, PI"o"". It is given by

H = min[I PLOSS] )=1

Where nline is number of transmission lines in the system. Before incorporating GA to optimal allocation of EG, some procedure need to be followed:

(1) Coding the variables into a finite string or chromosome (2) Mapping the objective function into a fitness form.

F= 1/(1+H)

88

The variables of the optimal allocation of EG problem are coded in the following manner. Firstly, each variable X is coded as the continuous floating numbers that range from 0 to 1. Then, the variables are concatenated to construct a multi variable string. The total multivariable or the length of chromosome is equal to (num�EG x 2) as shown in Fig. 7. Each EG representatives need to be multiplied with 2 because the first variable represents the location and the second one represents the size of EG.

EO location EO size

X\(L) X\(S) Xn(L) Xn(S)

X\(L)

��------------------��------------------�) y

Length=num_EO *2

Figure 6. Chromosome

While applying genetic algorithm for any problem initial population is generated randomly and later by using selection, crossover and mutation techniques we generate new chromosomes from old chromosomes. Detailed explanation of all genetic operators is given in [7].

IV. OPTIMAL ALLOCATION STUDY AND DISCUSSION

The method proposed has been tested on IEEE-69 bus radial distribution system. The algorithm has been programmed in MATLAB. The test system can be obtained in [12]. The GA properties are as follows

• Mutation probability, pm = 0.5 • Selection=0.5 • Population = 40 • Number of EG units = lor 2 • EG size = 0.01 MW < PEG < 2.5 MW The reason of using upto a maximum of two units of EG is

due to the cost of installation issue. Since this test system is moderate in size, it is adequate to install up to two units of EG.

oos

0 ...

Figure 7. Variation of total losses versus iterations


Fig.7 shows the variation of total losses versus iterations when 1 EG is installed into the system. Minimum active power losses obtained is 0.03l32 MW while connecting one EG unit at bus 61 with the size of active power l.9262 MW and reactive power 1.1936 MV AR. With the installation of EG unit at bus 61, losses are reduced to 90% when compared to a system without EG.

0.04 5,.---,------,------,------,------,------,--,-----,------,------,

0.04

0.035

O,OJ

002

0015

001

Figure 8. Variation of total losses versus iterations

Fig.8 shows the variation of total losses versus iterations when 2 EG's are installed into the system. Minimum active power losses obtained is 0.0119 MW while connecting one EG unit at bus 61 with the generating active power capacity of l.7251MW and reactive power capacity of l.0660 MVAR, 2nd

EG unit at bus 22 the generating active power capacity of 0.5398 MW and reactive power capacity of 0.3345 MV AR. With the installation of 2 EG units, losses are reduced to 96% when compared to a system without EG.

V. OPTIMAL ALLOCATION STUDY AND DISCUSSION

This section focuses on the IEEE 69-bus test system [11] .The analysis is done by introducing the contingency situation referred to as the fault between line 3 and 28.This is done to see the impact of EG that has or have been installed in the system. The study will emphasize on the changes of voltage profile of EG and the system losses before and after reconfigurations caused by the fault. However, the type of fault does not have any impact since it is assumed that the system is reconfigured after the isolation of the fault. This study may help in finding the trends of optimal size and location of EG in this test system. The number in the brackets is the bus number after reconfigurations have been done.

To observe the changes in voltage and losses due to the contingency and impact of EG, the study is divided into the following cases:

Case 1: Without EG and without the contingency.

Case 2: Without EG and with the contingency.

Case 3: With one unit of EG and without the contingency

Case 4: With one unit of EG and with the contingency.

89

Case 5: With two units of EG and without the contingency.

Case 6: With two units of EG and with the contingency.

VI. RESULTS

In this section, the graphs of variation of minimum bus voltage and total real power losses on test system before and after contingency are seen.

Case 1:

Bus No

Figure 9. Without EG and without the contingency.

Fig. 9 shows the voltage profile at each bus where the contingency and embedded generations are not considered. In this case, minimum bus voltage of 0.8759 pu at Bus 65 and total real power losses ofO.3153MW are obtained.

Case 2:

0.99

0,98

097

096

0.95

0.9.(

0 .93

092�················

O.91 0�--�--�="='----�--..,e_---+----_+__--_:70 BusNo

Figure 10. Without EG and with the contingency.

Fig. 10 shows the voltage profile at each bus where there is single contingency i.e. situation referred to as the fault between line 3 and 28, and no embedded generation unit is connected. In this case, minimum bus voltage of 0.9104 pu at Bus 26 and total real power losses of 0.2995 MW are obtained.


Case 3: 1�,-

----,-----,-----,-----.-----,------.----�

lOA

1.03

102

101

0.99 .

098

0.97

096:--0

____ ----:';;-____ ----:::-____ ----:':; ____ -----:'::--__ -----:':--__ -----,'-----__ -----,1

sus NO

Figure 11. With one unit of EG and without the contingency

Fig. 11 shows the comparison of voltage profiles at each bus for case 3 where there is no contingency and one embedded generation unit is connected at bus 61 with generating active power capacity 1.9262MW and reactive power capacity 1.1936MV AR. In this case we get minimum bus voltage 0.9668pu at bus no 61and total real power losses are 0.03132MW.

Case 4

0.«15

0.98

0975

0.97

0.965

0.96

0.955

0950�-------,;!;--------:;!;--------�-----c:-----+.------::O-----�70 BUS NO

Figure 12. With one unit of EG and with the contingency.

Fig. 12 shows the voltage profile at each bus where there is single contingency and one embedded generation unit is connected at Bus 60 with generating active power capacity of 2.2051 MW and reactive power capacity of 1.3662 MV AR. In this case, minimum bus voltage of 0.9538 pu at Bus 57 and total real power losses of 0.0719 MW are obtained.

90

Case 5:

BUSNO

Figure 13. With two units of EG and without the contingency.

Fig. 13 shows the voltage profile at each bus where there is no contingency and two embedded generation units are connected. One EG unit at Bus 61 with the generating active power capacity of 1.8718 MW and reactive power capacity of 1.4657 MV AR, 2nd EG unit at Bus 22 with the generating active power capacity of 0.4906 MW and reactive power capacity of 0.3039 MV AR. In this case, minimum bus voltage of 0.9821 pu at Bus 27 and total real power losses of 0.0114 MW are obtained.

Case 6: 1.025 ,---------,--------,-----,-----,-------,--------,------�

O.9750;--------;;;--------,:;!;____----,;,-----c:------f,50:---------:'�--�70 BUS NO

Figure 14. With two units of EG and with the contingency.

Fig. 14 shows the voltage profile at each bus where there is single contingency and two embedded generation units are connected. One EG unit at Bus 61 with the generating active power capacity of 1.8232 MW and reactive power capacity of 1.1299 MV AR, 2nd EG unit at Bus 49 with the generating active power capacity of 1.2164 MW and reactive power capacity of 0.7538 MVAR. In this case, minimum bus voltage of 0.9821 pu at Bus 27 and total real power losses of 0.0175 MW are obtained.


TABLE I. MINIMUM BUS VOLTAGES

No. of Minimum bus voltage

EG's Without Contingency With Contingency % Change

0 0.8759 0.9104 3

I 0.9668 0.9538 I

2 0.9821 0.9786 0.2

CONCLUSION

This paper has presented two main issues of Embedded Generation (EG) deployment in the distribution network. The first issue is the introduction of GA to find the optimal location and size of EG in the system. The continuous floating numbers are used as representation of the parameters in each chromosome. The single-point arithmetic crossover method that used in crossover process makes this approach success to find the best combination of location and sizing of EG simultaneously no matter how many units that needed to be deployed in the system.

The second issue is to see the impact of EG deployment when the contingency is created in the system. From the study, the installations of EG gives the better results in term of reduce system losses before and after contingency. The studies are demonstrated and tested on IEEE 69-bus distribution system. In this paper we use new distribution load flow solution for calculation losses and voltages of buses.

REFERENCES

[I] F. Sheidaei, M. Shadkam, and M. Zarei, "Optimal Distributed Generation allocation in distirbution systems employing ant colony to reduce losses," in Universities Power Engineering Conference, 2008. UPEC 2008. 43rd International, 2008, pp. 1-5.

91

[2] T. K. A Rahman, S. R. A Rahim, and I. Musirin, "Optimal allocation and sizing of embedded generators," in Power and Energy Conference, 2004. ECon 2004. Proceedings. National, 2004, pp. 288-294.

[3] M. F. AIHajri, M. R. AIRashidi, and M. E. El-Hawary, "Hybrid Particle Swarm Optimization Approach for Optimal Distribution Generation Sizing and Allocation in Distribution Systems," in Electrical and Computer Engineering, 2007. CCECE 2007. Canadian Conference from 22-26 April 2007, pp. 1290-1293.

[4] Y. Alinejad-Beromi, M. Sedighizadeh, M. R. Bayat, and M. E. Khodayar, "Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile," in Universities Power Engineering Conference, 2007. UPEC 2007. 42nd International, pp. 954-959.

[5] Z. Kai, A P. Agalgaonkar, K. M. Muttaqi, and S. Perera, "Voltage support by distributed generation units and shunt capacitors in distribution systems," in Power & Energy Society General Meeting, 2009. PES '09. IEEE, 2009, pp. 1-8.

[6] T.Nikram,AM Ranjbar,AR Shirani and, AOstadi "Optimal operation of distribution system with regard to Distributed generation: a comparison of evolutionary methods,". in Industry Applications Conference, 2005. Fourtieth lAS Annual Meeting. Conference Record of the 2005, pp. 2690-2697 Vol. 4.

[7] R. L. Haupt and S. E. Haupt, Practical genetic algorithms. New York: Wiley Pub, 1998.

[8] P. Chiradeja "Benefit of distributed generation: A line loss reduction analysis"2005 IEEE -Retransmission and Distribution conference and Exhibition: Asia and Pacific Dalian, China.

[9] D. Das, D.P. Kothari, A Kalam "Simple and efficient Method for load flow Solution of Radial distribution Networks", Journal of Electrical Power and Energy Systems, Vol 17, No.5, pp335 - 346, 1995.

[10] K.V.S Ramchandran Murthy ,M.Ramlingaraju,G.Govinda rao and K.Narasimha Rao"Topology based approach for Efficien Load flow Solution of Radial Distribution Networks" 16th National Power Systems Conference, I Sill _1711l DECEMBER, 2010, pp.176-179.

[II] M. H. Sulaiman, O. Aliman, and S. R. Abdul Rahim" contingency Analysis of Embedded Generation Deployment in Distribution Network" The 4th International Power Engineering and Optimization Conf. (PEOC02010), Shah Alam, Selangor, MALAYSIA: 23-24 June 201O,pp.412-416.

[12] R. Srinivasa Rao "Capacitor Placement in Radial Distribution System for Loss Reduction Using Artificial Bee Colony Algorithm" World Academy of Science, Engineering and Technology 68 2010, pp.288-292.

Optimal allocation and Contingency Analysis of Embedded Generation Deployment in Distribution...

Documents

Transcript of Optimal allocation and Contingency Analysis of Embedded Generation Deployment in Distribution...